HR: 1340h
AN: H13B-0411    [Abstracts]
TI: Search Strategy for a DNAPL Source
AU: * Dokou, Z
EM: zdokou@emba.uvm.edu
AF: Research Center for Groundwater Remediation Design, University of Vermont, 213 Votey Building University of Vermont, Burlington, VT 05405 United States
AU: Pinder, G
EM: pinder@emba.uvm.edu
AF: Research Center for Groundwater Remediation Design, University of Vermont, 213 Votey Building University of Vermont, Burlington, VT 05405 United States
AU: Ozbek, M
EM: ozbek@emba.uvm.edu
AF: Research Center for Groundwater Remediation Design, University of Vermont, 213 Votey Building University of Vermont, Burlington, VT 05405 United States
AB: The plume emanating from a DNAPL source is typically quite large and easily discovered as opposed to the identification of the location of a DNAPL source, which can be a very difficult task because it is a small target. The goal of this work is to identify the source of DNAPL contamination using an optimal search algorithm which exploits the above observation. The target locations of the possible sources are identified and given initial weights using an approach called information fusion. In this approach each possible source location is described by an n-dimensional vector, whose coordinates are values of identifying features of the source, such as its geography. Each feature value is compared with some prototype value, which gives a degree of confidence of the statement `source location i belongs to the group of true source locations'. Using a fuzzy integral all the individual degrees of confidence are combined and a global degree of confidence (weight) is assigned to each possible source. Given the initial identification of the sources the overall, the strategy uses stochastic groundwater flow and transport modeling under the assumption that hydraulic conductivity is known with uncertainty (Monte Carlo approach). The hydraulic conductivity realizations are obtained using the Latin hypercube sampling strategy. The algorithm defines how to achieve an acceptable level of source-location accuracy with the least possible number of water quality samples. Each new concentration sample selected is the one that reduces the total concentration variance the most. After each sample is taken the plume is updated using a Kalman Filter. The plumes emanating from each individual source are calculated using the Monte Carlo approach and are compared with the updated plume. The scores obtained from this comparison are used as input weights for each individual source, and the above steps are repeated until the optimal source location is found.
DE: 1829 Groundwater hydrology
DE: 1831 Groundwater quality
DE: 1832 Groundwater transport
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting